Words by Isabel O’Brien
A new survey has found that the use of artificial intelligence and real-world data (RWD) is on the rise across the biopharma industry, but significant hurdles remain for broader adoption.
The research, conducted by BioPharma Dive for health data firm TriNetX, polled 150 pharma and biotech leaders and found that three-quarters of organisations are already using RWD in drug development, with more than half integrating it with AI tools to accelerate insights.
Organisations are relying on RWD for a broad range of activities, including tracking medication use, supporting regulatory submissions and identifying participants for clinical trials. On average, companies are drawing data from more than five sources, including lab test results (77%), genomic information (62%), patient registries (61%) and health equity data (61%).
Steven Kundrot, Chief Operating Officer, TriNetX, described the shift as a turning point in how pharma companies operate: “Real-world data is no longer a concept, it’s a capability. The leaders we surveyed see its value and are investing in execution. But to fully realise real-world data’s promise, we must tackle integration challenges, enforce data standards and build trust in AI applications.”
However, data compatibility emerged as the top barrier to progress, cited by 29% of respondents. Kundrot stressed the need for greater harmonisation across data sources and urged organisations to work with trusted partners to maintain data fidelity and privacy.
The survey also underscored a growing commitment to more inclusive, patient-centric trials. A full 84% of executives reported increased efforts to improve participant diversity, and 99% said they plan to sustain or expand these efforts in the future.
“Regulatory uncertainty can stall inclusive trial design, even when the intent is there,” said Jeffrey Brown, PhD, Chief Scientific Officer, TriNetX. “Real-world data, especially social determinants of health, helps uncover patient realities and generate the kind of evidence regulators are looking for. It’s the bridge between inclusion goals and regulatory confidence.”
Encouragingly, all respondents also agreed that real-world evidence can improve the quality of regulatory submissions. But experts warn that its impact will ultimately depend on data quality and the integrity of study design.
As AI becomes more deeply embedded into the RWD landscape, the call for robust privacy protections and transparent AI governance is growing louder. Without these safeguards, Kundrot warned: “AI’s potential is enormous, but without trust and transparency in how data is processed and protected, adoption with falter.”
